Save Our Souls: Study of Twitter Use during India’s COVID-19 Pandemic

2023 | International Conference on Information Systems | Citations: 0

Authors: Bhattacharyya, Samadrita; Mojumder, Probal

Abstract: Twitter is a commonly used social platform for communication during disasters. ... Expand

Abstract: Twitter is a commonly used social platform for communication during disasters. Tweets by citizens during disasters to share information, seek, and offer help create a body of spontaneous, decentralized, emergent social media communication. Users' exploit Twitter's reach-enabling technological functionalities (hashtags (#), mentions (@), and 'reply-to') to draw attention to the messages. Set in context of the second wave of COVID-19 in India, that saw a surge in citizen-driven tweets seeking healthcare resources from fellow citizens and officials (i.e., SOS tweets), our paper empirically analyses the effects of Twitter's reach-enabling functionalities on online responses (i.e., retweets and replies) to these SOS tweets. We investigate the effects of inclusion of hashtags, mentions, and 'reply to' SOS tweets. We also examine the moderating effect of how the effects of the reach-enabling functionalities change as the social platform gets crowded with SOS tweets. The study offers various academic and practical implications. Collapse

Topics: Twitter pandemic social media Python societal evaluation criteria

Methods: propensity score method machine learning propensity score matching econometric modeling robustness check

Dissecting AI-Generated Fake Reviews: Detection and Analysis of GPT-Based Restaurant Reviews on Social Media

2023 | International Conference on Information Systems | Citations: 0

Authors: Gambetti, Alessandro; Han, Qiwei

Abstract: Recent advances in generative models such as GPT may be used to fabricate indis ... Expand

Abstract: Recent advances in generative models such as GPT may be used to fabricate indistinguishable fake customer reviews at a much lower cost, posing challenges for social media platforms to detect this kind of content. This study addresses two research questions: (1) the effective detection of AI-generated restaurant reviews generated from high-quality elite authentic reviews, and (2) the comparison of out-of-sample predicted AI-generated reviews and authentic reviews across multiple dimensions of review, user, restaurant, and content characteristics. We fine-tuned a GPT text detector to predict fake reviews, significantly outperforming existing solutions. We applied the model to predict non-elite reviews that already passed the Yelp filtering system, revealing that AI-generated reviews typically score higher ratings, users posting such content have less established Yelp reputations and AI-generated reviews are more comprehensible and less linguistically complex than human-generated reviews. Notably, machine-generated reviews are more prevalent in low-traffic restaurants in terms of customer visits. Collapse

Topics: fake review artificial intelligence application programming interface open source pandemic

Methods: machine learning survey computational algorithm sensitivity analysis ANOVA

Is a Pretrained Model the Answer to Situational Awareness Detection on Social Media?

2023 | HICSS | Citations: 0

Authors: Lo, S.; Lee, K.; Zhang, Y.

Abstract: Social media can be valuable for extracting information about an event or incid ... Expand

Abstract: Social media can be valuable for extracting information about an event or incident on the ground. However, the vast amount of content shared, and the linguistic variants of languages used on social media make it challenging to identify important situational awareness content to aid in decision-making for first responders. In this study, we assess whether pretrained models can be used to address the aforementioned challenges on social media. Various pretrained models, including static word embedding (such as Word2Vec and GloVe) and contextualized word embedding (such as DistilBERT) are studied in detail. According to our findings, a vanilla DistilBERT pretrained language model is insufficient to identify situation awareness information. Fine-tuning by using datasets of various event types and vocabulary extension is essential to adapt a DistilBERT model for real-world situational awareness detection. Collapse

Topics: Twitter situational awareness social media Wikipedia wiki

Methods: BERT DistilBERT support vector machine machine learning word embedding

Mapping the Emotional Journey of STD patients in Online Forums

2023 | Americas Conference on Information Systems | Citations: 0

Authors: THIMMANAYAKANAPALYA, Sagarika Suresh; Sharman, Raj; Mulgund, Pavankumar; Parthiban, Lakshmi Prithika; Samant, Harshada

Abstract: People inflicted with diseases such as STDs experience a tremendous number of v ... Expand

Abstract: People inflicted with diseases such as STDs experience a tremendous number of varying emotions from the onset of symptoms to being cured/living with it. Leveraging the power of bidirectional encoder representation transformers our study builds a custom-named entity recognition model which identifies uniquely associated emotions at various stages of the disease. Our study uses over 1 million posts derived from online communities regarding STDs. We further provide analytical insights into varying emotions at different stages based on contextual reasonings. Finally, we utilize the resulting insights to produce prescriptive measures for future online recommendation systems on online communities, and for stakeholders hoping to build devices that address problems about stigmatized disorders such as STDs. Collapse

Topics: online community recommender system misinformation website Reddit

Methods: BERT computational algorithm named entity recognition machine learning language model

Living in a Fishbowl or Not: The Role of Transparency and Privacy in Creative Dialogues on Enterprise Social Media

2023 | Journal of the Association for Information Systems | Citations: 0

Authors: Van Osch, Wietske; Bulgurcu, Burcu; Liang, Yuyang

Abstract: Transparency-the observability of activities, behaviors, and performance-is oft ... Expand

Abstract: Transparency-the observability of activities, behaviors, and performance-is often treated as a panacea for modern management. Yet there is a conundrum in the literature, with some studies suggesting that transparency may benefit group creativity and others suggesting that privacy may do so. A similar conundrum exists regarding the effects of different social capital types-structural holes vs. network cohesion-on group creativity. Enterprise social media (ESM) provide a unique opportunity to solve these conundrums by allowing groups to be "transparent" (non-group members can observe and/or participate in group activities) or "private" (group members and activities are hidden from the community) and enabling groups to develop distinct social capital structures. Using data from 28,083 written interactions produced by 109 transparent and 106 private groups in an ESM of a multinational design firm, we found strong support for our contingency hypotheses that both transparent and private groups may produce high levels of creative dialogues, yet in different forms. Specifically, expansion-focused creative dialoguesthose focused on combining or expanding existing concepts-emerge in transparent groups, but only when the group's social capital is characterized by structural holes. Conversely, we found that reframingfocused dialogues-those focused on challenging and rethinking-emerge in private groups but only when the group's social capital is characterized by network cohesion. Theoretically, these findings can help to solve the conundrums in the literature on group creativity and shed light on the role of ESM use in this context. Practically, our findings offer a critical reflection o contemporary initiatives for increasing transparency, whether through physical design or digital transformation.Burcu Bulgurcu is an assistant professor of information systems in the Department of Information Technology Management of the Ted Rogers School of Management. She was formerly a professor at the Carroll School of Management, Boston College. Her research focuses on the behavioral and managerial aspects of cybersecurity and information privacy, security and privacy by design, and the implications of privacy on creativity and innovation in digital enterprises. The results of her research have been published in the Financial Times Top 50 journals and leading conferences, including MIS Quarterly, the Journal of Management Information Systems, and ICIS and AMCIS proceedings. Dr. Bulgurcu's research has been supported by the Social Sciences and Humanities Research Council of Canada and she has received numerous academic honors and awards. She is currently teaching award-winning courses on strategic IT management and data visualization.Yuyang Liang is a postdoctoral scholar in the Department of Information Technology at HEC Montréal. He received his PhD in media and information studies in the Department of Media and Information at Michigan State University. His areas of interest include knowledge sharing and collaboration via online communities and social media, enterprise social media, and computational modeling of social systems. His work has appeared in top computer-supported cooperative work and information system Collapse

Topics: social capital privacy blog spreadsheet open source

Methods: computational algorithm machine learning theory development qualitative coding descriptive statistic

Theories: theory of conceptual combinations

LSTM-ReGAT: A network-centric approach for cryptocurrency price trend prediction

2023 | Decision Support Systems | Citations: 0

Authors: Zhong, Chao; Du, Wei; Xu, Wei; Huang, Qianhui; Zhao, Yinuo; Wang, Mingming

Abstract: Predicting price trends of cryptocurrencies is a challenging task due to the hig ... Expand

Abstract: Predicting price trends of cryptocurrencies is a challenging task due to the highly speculative cryptocurrency market. Prior studies mainly investigate predictors such as historical trading data, macroeconomic development, and public interests in cryptocurrencies for price trend prediction while ignoring the predictive role of the re­ lations between cryptocurrencies. In fact, the price movement of a cryptocurrency may be affected by those of other cryptocurrencies, thus, incorporating cryptocurrency interrelations can further improve the prediction performance. In this paper, we propose a novel end-to-end model for price trend prediction using long short-term memory (LSTM) and relationwise graph attention network (ReGAT), in which both individual cryptocurrency features and cryptocurrency relations are considered. Specifically, a cryptocurrency network is built by using shared features between cryptocurrencies. Further, LSTM is used to profile sequential patterns of individual cryptocurrency features. To fully extract network features, ReGAT is designed to aggregate information conveyed by different types of relations while automatically differentiating their importance. Finally, the conventional and network features are concatenated to predict the price trend. The effectiveness of LSTM-ReGAT is validated using real-world cryptocurrency market data. The trading simulations for Bitcoin and portfolios reveal that our model obtains the highest profits. Our study provides insightful implications for investment decision support in cryp­ tocurrency market. Collapse

Topics: cryptocurrency Bitcoin database system Google+ information system use

Methods: machine learning time series analysis longitudinal research simulation support vector machine

Heterogeneous Energy-aware Load Balancing for Industry 4.0 and IoT Environments

2022 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Ahmed, Usman; Lin, Jerry Chun-Wei; Srivastava, Gautam

Abstract: With the improvement of global infrastructure, Cyber-Physical Systems (CPS) have ... Expand

Abstract: With the improvement of global infrastructure, Cyber-Physical Systems (CPS) have become an important component of Industry 4.0. Both the application as well as the machine work together to improve the task of interdependencies. Machine learning methods in CPS require the monitoring of computational algorithms, including adopting optimizations, fine-tuning cyber systems, improving resource utilization, as well as reducing vulnerability and also computation time. By leveraging the tremendous parallelism provided by General-Purpose Graphics Processing Units (GPGPU) as well as OpenCL, it is possible to dramatically reduce the execution time of data-parallel programs. However, when running an application with tiny amounts of data on a GPU, GPU resources are wasted because the program may not be able to fully utilize the GPU cores. This is because there is no mechanism for kernels to share a GPU due to the lack of OS support for GPUs. Optimal device selection is required to reduce the high power of the GPU. In this paper, we propose an energy reduction method for heterogeneous clustering. This study focuses on load balancing; resource-aware processor selection based on machine learning is performed using code features. The proposed method identifies energy-efficient kernel candidates (from the employment pool). Then, it selects a pair of kernel candidates from all possibilities that lead to a reduction in both energy consumption as well as execution time. Experimental results show that the proposed kernel approach reduces execution time by 2.23 times compared to a baseline scheduling system. Experiments have also shown that the execution time is 1.2 times faster than state-of-the-art approaches. Collapse

Topics: production management computer hardware database system internet of things mobile system

Methods: computational algorithm machine learning experiment random forest classification gradient boosting

Transformer-based Summarization and Sentiment Analysis of SEC 10-K Annual Reports for Company Performance Prediction

2022 | HICSS | Citations: 0

Authors: Hsieh, Hsin-Ting; Hristova, Diana

Abstract: Annual reports published by companies contain important insights regarding their ... Expand

Abstract: Annual reports published by companies contain important insights regarding their performance and are often analyzed in a manual, subjective manner. We address this point by combining the streams of research on text summarization and topic modelling with the one on sentiment analysis. Our approach consists of the steps of text summarization using BERTSUMEXT, topic modelling with LDA, sentiment analysis with FinBERT, and performance prediction with Decision Trees and Random Forest. The result provides decision makers with an interpretable and condensed representation of the content of annual reports, together with its relationship to future company performance. We evaluate our approach on 10-K reports, demonstrating both its interpretability for analysts and explanatory power regarding future company performance. Collapse

Topics: organizational productivity data breach cloud computing Python explainable artificial intelligence

Methods: sentiment analysis topic model machine learning BERT decision tree classification

Line Faults Classification Using Machine Learning on Three Phase Voltages Extracted from Large Dataset of PMU Measurements

2022 | HICSS | Citations: 15

Authors: Otudi, Hussain; Dokic, Tatjana; Mohamed, Taif; Kezunovic, Mladen; Hu, Yi; Obradovic, Zoran

Abstract: An end-to-end supervised learning method was developed to classify transmission ... Expand

Abstract: An end-to-end supervised learning method was developed to classify transmission line faults in a twoyear field-recorded dataset that includes synchronized measurements of three-phase voltages recorded by 38 phasor measurement units (PMUs) sparsely located in the US Western Grid interconnection. Statistical analysis was performed to extract features from this large dataset to train the support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) classifiers. The training further leverages a simulated dataset from a synthetic grid with 12 PMUs to increase the number of types of faults infrequently seen in the field-recorded dataset. Training the classification models with the combined dataset resulted in a classification accuracy of 98.58%. This is a significant improvement over 86.87% to 87.17% accuracy obtained by relying on the field-recorded dataset alone. Collapse

Topics: Python data integration Apache Spark

Methods: simulation support vector machine XGBoost experiment computational algorithm

Universal Spam Detection using Transfer Learning of BERT Model

2022 | HICSS | Citations: 0

Authors: Tida, Vijay Srinivas; Hsu, Sonya Hy

Abstract: Deep learning transformer models become important by training on text data based ... Expand

Abstract: Deep learning transformer models become important by training on text data based on self-attention mechanisms. This manuscript demonstrated a novel universal spam detection model using pre-trained Google's Bidirectional Encoder Representations from Transformers (BERT) base uncased models with four datasets by efficiently classifying ham or spam emails in real-time scenarios. Collapse

Topics: email spam Google+ electronic mail fake news Wikipedia

Methods: machine learning BERT computational algorithm transformer model deep learning